##################################################################
##################################################################
## Replication Material
## Widmann & Wich: Creating and Comparing Dictionary, Word Embedding, and Transformer-based 
## Models to Measure Discrete Emotions in German Political Text
## Political Analysis
## tobias.widmann@eui.eu
##
## Script 03: Training Machine Learning Models
##################################################################
##################################################################

# Note: The file 000_readme.pdf describes all scripts and datasets required to replicate the analysis

# This script was run on the following R version, platform and OS:
# R version 4.0.5 (2021-03-31)
# Platform: x86_64-apple-darwin17.0 (64-bit)
# Running under: macOS Big Sur 11.5.1

sessionInfo()

#### Set Working Directory to the Replication Folder ############################

# Delete hashtag below and fill in the directory of the replication folder
#setwd("")



#### Load Packages ##############################################################

library(keras)        # Version 2.6.0
library(quanteda)     # Version 3.0.0
library(corpus)       # Version 0.10.1
library(glmnet)       # Version 4.1-1
library(e1071)        # Version 1.7-6
library(reticulate)   # Version 1.21
library(randomForest) # Version 4.6-14
library(readr)      # Version 1.4.0


#### Load Data #################################################################
load("./data1_prepared.RData")

#### Training and Test data
set.seed(1111)
sample_size <- floor(.90 * nrow(data1_prepared))
train_ind <- sample(nrow(data1_prepared), size = sample_size)

test_data <- data1_prepared[-train_ind,]
training_data <- data1_prepared[train_ind,]


## function to compute accuracy
acc_fun <- function(ypred, y){
  tab <- table(ypred, y)
  return(sum(diag(tab))/sum(tab))
}
# function to compute precision
prec_fun <- function(ypred, y){
  tab <- table(ypred, y)
  return((tab[2,2])/(tab[2,1]+tab[2,2]))
}
# function to compute recall
rec_fun <- function(ypred, y){
  tab <- table(ypred, y)
  return(tab[2,2]/(tab[1,2]+tab[2,2]))
}



#### Neural Network Model ################################################
##### Create Word Embeddings #############################

# First, we create a corpus and pre-process this corpus in order to increase matches
cgcorpus <- corpus(data1_prepared$Text)
cgdfm <- dfm(cgcorpus, remove=stopwords("german"), verbose=TRUE, tolower = TRUE)

# Stemming
cgdfm <- dfm_wordstem(cgdfm, language = "german")

#Then, we will convert the word embeddings to a data frame, and then we will 
#match the features from each document with their corresponding embeddings.

# Load locally trained word embeddings
w2v <- readr::read_delim("./vec_ed_preprocessed.txt", 
                         skip=1, delim=" ", quote="",
                         col_names=c("word", paste0("V", 1:100)))

# Stemming
w2v$word <- text_tokens(w2v$word, stemmer = "de")

# Matching
w2v <- w2v[w2v$word %in% featnames(cgdfm),]


# creating new feature matrix for embeddings
embed <- matrix(NA, nrow=ndoc(cgdfm), ncol=100)
for (i in 1:ndoc(cgdfm)){
  if (i %% 100 == 0) message(i, '/', ndoc(cgdfm))
  # extract word counts
  vec <- as.numeric(cgdfm[i,])
  # keep words with counts of 1 or more
  doc_words <- featnames(cgdfm)[vec>0]
  # extract embeddings for those words
  embed_vec <- w2v[w2v$word %in% doc_words, 2:101]
  # aggregate from word- to document-level embeddings by taking AVG
  embed[i,] <- colMeans(embed_vec, na.rm=TRUE)
  # if no words in embeddings, simply set to 0
  if (nrow(embed_vec)==0) embed[i,] <- 0
}

##### Train Models ################################################
# Now, after we created embeddings per sentence, we can use these to train the neeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee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eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee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network

# Anger
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_anger[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_anger[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

model_anger <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(model_anger, "./keras_anger90", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Fear
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_fear[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_fear[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 256, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 128, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

model_fear <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(model_fear, "./keras_fear90", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Disgust
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_disgust[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_disgust[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

model_disgust <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(model_disgust, "./keras_disgust90", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Sadness
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_sadness[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_sadness[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

model_sadness <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(model_sadness, "./keras_sadness90", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)


#Joy
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_joy[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_joy[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 64, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 32, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

model_joy <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(model_joy, "./keras_joy90", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)


#Enthusiasm
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_enthusiasm[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_enthusiasm[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 256, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 128, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

model_enthusiasm <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(model_enthusiasm, "./keras_enthusiasm90", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Pride
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_pride[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_pride[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 256, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 128, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

model_pride <- model

# Finally, we can save the model to apply it to new data
#save_model_hdf5(model_pride, "./keras_pride90", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Hope
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_hope[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_hope[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 64, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 32, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

model_hope <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(model_hope, "./keras_hope90", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)



#### Bojanowski et al. ##################################################
##### Create Word Embeddings #############################

# First, we create a corpus and pre-process this corpus in order to increase matches
cgcorpus <- corpus(data1_prepared$Text)
cgdfm <- dfm(cgcorpus, remove=stopwords("german"), verbose=TRUE, tolower = TRUE)

# Stemming
cgdfm <- dfm_wordstem(cgdfm, language = "german")

# Then, we will convert the word embeddings to a data frame, and then we will 
# match the features from each document with their corresponding embeddings.

# Load in the word embeddings
# Note: this step takes a long time

lines <- readLines("./boja_wiki.de.vec")

df1 <- read.table(text=lines, sep="", quote="", fill = TRUE)

head(df1)
colnames(df1)[1] <- "word"

# Stemming
df1$word <- text_tokens(df1$word, stemmer = "de")

# Matching
w2v2 <- df1[df1$word %in% featnames(cgdfm),]

# creating new feature matrix for embeddings
embed_boja <- matrix(NA, nrow=ndoc(cgdfm), ncol=300)
for (i in 1:ndoc(cgdfm)){
  if (i %% 100 == 0) message(i, '/', ndoc(cgdfm))
  # extract word counts
  vec <- as.numeric(cgdfm[i,])
  # keep words with counts of 1 or more
  doc_words <- featnames(cgdfm)[vec>0]
  # extract embeddings for those words
  embed_vec <- w2v2[w2v2$word %in% doc_words, 2:301]
  # aggregate from word- to document-level embeddings by taking AVG
  embed_boja[i,] <- colMeans(embed_vec, na.rm=TRUE)
  # if no words in embeddings, simply set to 0
  if (nrow(embed_vec)==0) embed_boja[i,] <- 0
}


##### Train Model #######################################################################
# Now, after we created embeddings per sentence, we can use these to train the neural network

# Anger
x_train <- embed_boja[train_ind,]
y_train <- to_categorical(data1_prepared$hf_anger[train_ind])
x_test <- embed_boja[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_anger[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

boja_anger <- model

# Finally, we can save the model to apply it to new data
#save_model_hdf5(boja_anger, "./boja_keras_anger", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Fear
x_train <- embed_boja[train_ind,]
y_train <- to_categorical(data1_prepared$hf_fear[train_ind])
x_test <- embed_boja[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_fear[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

boja_fear <- model

# Finally, we can save the model to apply it to new data
#save_model_hdf5(boja_fear, "./boja_keras_fear", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Disgust
x_train <- embed_boja[train_ind,]
y_train <- to_categorical(data1_prepared$hf_disgust[train_ind])
x_test <- embed_boja[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_disgust[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

boja_disgust <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(boja_disgust, "./boja_keras_disgust", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Sadness
x_train <- embed_boja[train_ind,]
y_train <- to_categorical(data1_prepared$hf_sadness[train_ind])
x_test <- embed_boja[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_sadness[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

boja_sadness <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(boja_sadness, "./boja_keras_sadness", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)


#Joy
x_train <- embed_boja[train_ind,]
y_train <- to_categorical(data1_prepared$hf_joy[train_ind])
x_test <- embed_boja[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_joy[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

boja_joy <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(boja_joy, "./boja_keras_joy", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)


#Enthusiasm
x_train <- embed_boja[train_ind,]
y_train <- to_categorical(data1_prepared$hf_enthusiasm[train_ind])
x_test <- embed_boja[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_enthusiasm[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

boja_enthusiasm <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(boja_enthusiasm, "./boja_keras_enthusiasm", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Pride
x_train <- embed_boja[train_ind,]
y_train <- to_categorical(data1_prepared$hf_pride[train_ind])
x_test <- embed_boja[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_pride[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

boja_pride <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(boja_pride, "./boja_keras_pride", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Hope
x_train <- embed_boja[train_ind,]
y_train <- to_categorical(data1_prepared$hf_hope[train_ind])
x_test <- embed_boja[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_hope[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

boja_hope <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(boja_hope, "./boja_keras_hope", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)



#### Mikolov et al. Model ################################################
##### Create Word Embeddings #############################

# First, we create a corpus and pre-process this corpus in order to increase matches
cgcorpus <- corpus(data1_prepared$Text)
cgdfm <- dfm(cgcorpus, remove=stopwords("german"), verbose=TRUE, tolower = TRUE)

# Stemming
cgdfm <- dfm_wordstem(cgdfm, language = "german")

# Then, we will convert the word embeddings to a data frame, and then we will 
# match the features from each document with their corresponding embeddings.

# Load in the word embeddings
lines <- readLines("./miko_cc.de.300.vec")

df1 <- read.table(text=lines, sep="", quote="", fill = TRUE, skip = 1)
head(df1)
colnames(df1)[1] <- "word"

# Stem embeddings
df1$word <- text_tokens(df1$word, stemmer = "de")

# Match embeddings with corpus
w2v2 <- df1[df1$word %in% featnames(cgdfm),]

# creating new feature matrix for embeddings
embed_miko <- matrix(NA, nrow=ndoc(cgdfm), ncol=300)
for (i in 1:ndoc(cgdfm)){
  if (i %% 100 == 0) message(i, '/', ndoc(cgdfm))
  # extract word counts
  vec <- as.numeric(cgdfm[i,])
  # keep words with counts of 1 or more
  doc_words <- featnames(cgdfm)[vec>0]
  # extract embeddings for those words
  embed_vec <- w2v2[w2v2$word %in% doc_words, 2:301]
  # aggregate from word- to document-level embeddings by taking AVG
  embed_miko[i,] <- colMeans(embed_vec, na.rm=TRUE)
  # if no words in embeddings, simply set to 0
  if (nrow(embed_vec)==0) embed_miko[i,] <- 0
}



##### Train Model #######################################################################
# Now, after we created embeddings per sentence, we can use these to train the neural network

# Anger
x_train <- embed_miko[train_ind,]
y_train <- to_categorical(data1_prepared$hf_anger[train_ind])
x_test <- embed_miko[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_anger[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

miko_anger <- model

# Finally, we can save the model to apply it to new data
#save_model_hdf5(miko_anger, "./miko_keras_anger", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Fear
x_train <- embed_miko[train_ind,]
y_train <- to_categorical(data1_prepared$hf_fear[train_ind])
x_test <- embed_miko[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_fear[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

miko_fear <- model

# Finally, we can save the model to apply it to new data
#save_model_hdf5(miko_fear, "./miko_keras_fear", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Disgust
x_train <- embed_miko[train_ind,]
y_train <- to_categorical(data1_prepared$hf_disgust[train_ind])
x_test <- embed_miko[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_disgust[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

miko_disgust <- model

# Finally, we can save the model to apply it to new data
#save_model_hdf5(miko_disgust, "./miko_keras_disgust", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Sadness
x_train <- embed_miko[train_ind,]
y_train <- to_categorical(data1_prepared$hf_sadness[train_ind])
x_test <- embed_miko[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_sadness[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

miko_sadness <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(miko_sadness, "./miko_keras_sadness", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)


#Joy
x_train <- embed_miko[train_ind,]
y_train <- to_categorical(data1_prepared$hf_joy[train_ind])
x_test <- embed_miko[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_joy[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

miko_joy <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(miko_joy, "./miko_keras_joy", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)


#Enthusiasm
x_train <- embed_miko[train_ind,]
y_train <- to_categorical(data1_prepared$hf_enthusiasm[train_ind])
x_test <- embed_miko[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_enthusiasm[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

miko_enthusiasm <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(miko_enthusiasm, "./miko_keras_enthusiasm", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Pride
x_train <- embed_miko[train_ind,]
y_train <- to_categorical(data1_prepared$hf_pride[train_ind])
x_test <- embed_miko[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_pride[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

miko_pride <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(miko_pride, "./miko_keras_pride", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Hope
x_train <- embed_miko[train_ind,]
y_train <- to_categorical(data1_prepared$hf_hope[train_ind])
x_test <- embed_miko[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_hope[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

miko_hope <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(miko_hope, "./miko_keras_hope", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)



#### Polyglot Model #######################################################
##### Create Word Embeddings #############################

# First, we create a corpus and pre-process this corpus in order to increase matches
cgcorpus <- corpus(data1_prepared$Text)
cgdfm <- dfm(cgcorpus, remove=stopwords("german"), verbose=TRUE, tolower = TRUE)

# Stemming
cgdfm <- dfm_wordstem(cgdfm, language = "german")

# Then, we will convert the word embeddings to a data frame, and then we will 
# match the features from each document with their corresponding embeddings.

source_python("./read_pickle.py")
pickle_data <- read_pickle_file("./polyglot-de.pkl")

# Then we convert the word embeddings to a data frame, and then we will 

polyglot <- data.frame(pickle_data[2][[1]])
wordlist <- as.list(pickle_data[[1]])
polyglot$word <- wordlist
polyglot <- polyglot[,c(65,1:64)]

# Then we match the features from each document with their corresponding embeddings
# To increase matches we stem them first

polyglot$word <- text_tokens(polyglot$word, stemmer = "de")

w2v2 <- polyglot[polyglot$word %in% featnames(cgdfm),]

# creating new feature matrix for embeddings
embed_poly <- matrix(NA, nrow=ndoc(cgdfm), ncol=64)
for (i in 1:ndoc(cgdfm)){
  if (i %% 100 == 0) message(i, '/', ndoc(cgdfm))
  # extract word counts
  vec <- as.numeric(cgdfm[i,])
  # keep words with counts of 1 or more
  doc_words <- featnames(cgdfm)[vec>0]
  # extract embeddings for those words
  embed_vec <- w2v2[w2v2$word %in% doc_words, 2:65]
  # aggregate from word- to document-level embeddings by taking AVG
  embed_poly[i,] <- colMeans(embed_vec, na.rm=TRUE)
  # if no words in embeddings, simply set to 0
  if (nrow(embed_vec)==0) embed_poly[i,] <- 0
}




##### Train Model #######################################################################
# Now, after we created embeddings per sentence, we can use these to train the neural network


# Anger
x_train <- embed_poly[train_ind,]
y_train <- to_categorical(data1_prepared$hf_anger[train_ind])
x_test <- embed_poly[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_anger[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

poly_anger <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(poly_anger, "./poly_keras_anger", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Fear
x_train <- embed_poly[train_ind,]
y_train <- to_categorical(data1_prepared$hf_fear[train_ind])
x_test <- embed_poly[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_fear[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

poly_fear <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(poly_fear, "./poly_keras_fear", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Disgust
x_train <- embed_poly[train_ind,]
y_train <- to_categorical(data1_prepared$hf_disgust[train_ind])
x_test <- embed_poly[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_disgust[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

poly_disgust <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(poly_disgust, "./poly_keras_disgust", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Sadness
x_train <- embed_poly[train_ind,]
y_train <- to_categorical(data1_prepared$hf_sadness[train_ind])
x_test <- embed_poly[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_sadness[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

poly_sadness <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(poly_sadness, "./poly_keras_sadness", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)


#Joy
x_train <- embed_poly[train_ind,]
y_train <- to_categorical(data1_prepared$hf_joy[train_ind])
x_test <- embed_poly[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_joy[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

poly_joy <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(poly_joy, "./poly_keras_joy", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)


#Enthusiasm
x_train <- embed_poly[train_ind,]
y_train <- to_categorical(data1_prepared$hf_enthusiasm[train_ind])
x_test <- embed_poly[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_enthusiasm[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

poly_enthusiasm <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(poly_enthusiasm, "./poly_keras_enthusiasm", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Pride
x_train <- embed_poly[train_ind,]
y_train <- to_categorical(data1_prepared$hf_pride[train_ind])
x_test <- embed_poly[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_pride[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

poly_pride <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(poly_pride, "./poly_keras_pride", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)

#Hope
x_train <- embed_poly[train_ind,]
y_train <- to_categorical(data1_prepared$hf_hope[train_ind])
x_test <- embed_poly[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_hope[-train_ind])
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 128, activation = 'relu', input_shape = ncol(x_train)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 64, activation = 'relu') %>%
  layer_dropout(rate = 0.4) %>%
  layer_dense(units = 2, activation = 'softmax')
summary(model)
model %>% compile(
  loss = 'binary_crossentropy',
  optimizer = 'adam',
  metrics = c('accuracy')
)
history <- model %>% fit(
  x_train, y_train, 
  epochs = 25, 
  validation_split = 0.1
)
model %>% evaluate(x_test, y_test)

poly_hope <- model
# Finally, we can save the model to apply it to new data
#save_model_hdf5(poly_hope, "./poly_keras_hope", overwrite = FALSE, include_optimizer = TRUE, overwrite = FALSE)



#### Lasso Model #########################################################
##### Create Word Embeddings #############################


# First, we build a corpus of our training and test data
cgcorpus <- corpus(data1_prepared$Text)
cgdfm <- dfm(cgcorpus, remove=stopwords("german"), verbose=TRUE, tolower = TRUE)

# Stemming
cgdfm <- dfm_wordstem(cgdfm, language = "german")

#First, we will convert the word embeddings to a data frame, and then we will 
#match the features from each document with their corresponding embeddings.

# extracting word embeddings for words in corpus (local word embeddings)
w2v <- readr::read_delim("./vec_ed_preprocessed.txt", 
                         skip=1, delim=" ", quote="",
                         col_names=c("word", paste0("V", 1:100)))

# Stemming
w2v$word <- text_tokens(w2v$word, stemmer = "de")

# Matching
w2v <- w2v[w2v$word %in% featnames(cgdfm),]


# creating new feature matrix for embeddings
embed <- matrix(NA, nrow=ndoc(cgdfm), ncol=100)
for (i in 1:ndoc(cgdfm)){
  if (i %% 100 == 0) message(i, '/', ndoc(cgdfm))
  # extract word counts
  vec <- as.numeric(cgdfm[i,])
  # keep words with counts of 1 or more
  doc_words <- featnames(cgdfm)[vec>0]
  # extract embeddings for those words
  embed_vec <- w2v[w2v$word %in% doc_words, 2:101]
  # aggregate from word- to document-level embeddings by taking AVG
  embed[i,] <- colMeans(embed_vec, na.rm=TRUE)
  # if no words in embeddings, simply set to 0
  if (nrow(embed_vec)==0) embed[i,] <- 0
}


##### Train Model #######################################################################
# Now, after we created embeddings per sentence, we can use these to train the lasso classifiers

#Anger
lasso <- NULL
lasso <- cv.glmnet(embed[train_ind,], data1_prepared$hf_anger[train_ind], 
                   family="binomial", alpha=1, nfolds=100, parallel=TRUE, intercept=FALSE,
                   type.measure="class")

lasso_anger <- lasso
# save(lasso_anger, "./lasso_anger.Rdata")

#fear
lasso <- NULL
lasso <- cv.glmnet(embed[train_ind,], data1_prepared$hf_fear[train_ind], 
                   family="binomial", alpha=1, nfolds=100, parallel=TRUE, intercept=FALSE,
                   type.measure="class")

lasso_fear <- lasso
# save(lasso_fear, "./lasso_fear.Rdata")

#disgust
lasso <- NULL
lasso <- cv.glmnet(embed[train_ind,], data1_prepared$hf_disgust[train_ind], 
                   family="binomial", alpha=1, nfolds=100, parallel=TRUE, intercept=FALSE,
                   type.measure="class")

lasso_disgust <- lasso
# save(lasso_disgust, "./lasso_disgust.Rdata")

#sadness
lasso <- NULL
lasso <- cv.glmnet(embed[train_ind,], data1_prepared$hf_sadness[train_ind], 
                   family="binomial", alpha=1, nfolds=100, parallel=TRUE, intercept=FALSE,
                   type.measure="class")

lasso_sadness <- lasso
# save(lasso_sadness, "./lasso_sadness.Rdata")

#joy
lasso <- NULL
lasso <- cv.glmnet(embed[train_ind,], data1_prepared$hf_joy[train_ind], 
                   family="binomial", alpha=1, nfolds=100, parallel=TRUE, intercept=FALSE,
                   type.measure="class")

lasso_joy <- lasso
# save(lasso_joy, "./lasso_joy.Rdata")

#enthusiasm
lasso <- NULL
lasso <- cv.glmnet(embed[train_ind,], data1_prepared$hf_enthusiasm[train_ind], 
                   family="binomial", alpha=1, nfolds=100, parallel=TRUE, intercept=FALSE,
                   type.measure="class")

lasso_enthusiasm <- lasso
# save(lasso_enthusiasm, "./lasso_enthusiasm.Rdata")

#pride
lasso <- NULL
lasso <- cv.glmnet(embed[train_ind,], data1_prepared$hf_pride[train_ind], 
                   family="binomial", alpha=1, nfolds=100, parallel=TRUE, intercept=FALSE,
                   type.measure="class")

lasso_pride <- lasso
# save(lasso_pride, "./lasso_pride.Rdata")

#hope
lasso <- NULL
lasso <- cv.glmnet(embed[train_ind,], data1_prepared$hf_hope[train_ind], 
                   family="binomial", alpha=1, nfolds=100, parallel=TRUE, intercept=FALSE,
                   type.measure="class")


lasso_hope <- lasso
# save(lasso_hope, "./lasso_hope.Rdata")


#### Naive Bayes Model ###################################################
##### Create Word Embeddings #############################


# First, we build a corpus of our training and test data
cgcorpus <- corpus(data1_prepared$Text)
cgdfm <- dfm(cgcorpus, remove=stopwords("german"), verbose=TRUE, tolower = TRUE)

# Stemming
cgdfm <- dfm_wordstem(cgdfm, language = "german")

#First, we will convert the word embeddings to a data frame, and then we will 
#match the features from each document with their corresponding embeddings.

# extracting word embeddings for words in corpus (local word embeddings)
w2v <- readr::read_delim("./vec_ed_preprocessed.txt", 
                         skip=1, delim=" ", quote="",
                         col_names=c("word", paste0("V", 1:100)))

# Stemming
w2v$word <- text_tokens(w2v$word, stemmer = "de")

# Matching
w2v <- w2v[w2v$word %in% featnames(cgdfm),]


# creating new feature matrix for embeddings
embed <- matrix(NA, nrow=ndoc(cgdfm), ncol=100)
for (i in 1:ndoc(cgdfm)){
  if (i %% 100 == 0) message(i, '/', ndoc(cgdfm))
  # extract word counts
  vec <- as.numeric(cgdfm[i,])
  # keep words with counts of 1 or more
  doc_words <- featnames(cgdfm)[vec>0]
  # extract embeddings for those words
  embed_vec <- w2v[w2v$word %in% doc_words, 2:101]
  # aggregate from word- to document-level embeddings by taking AVG
  embed[i,] <- colMeans(embed_vec, na.rm=TRUE)
  # if no words in embeddings, simply set to 0
  if (nrow(embed_vec)==0) embed[i,] <- 0
}


##### Train Model #######################################################################
# Now, after we created embeddings per sentence, we can use these to train the Naive Bayes classifiers

#anger
classifier_nb <- naiveBayes(data1_prepared$hf_anger[train_ind] ~ ., data = as.data.frame(as.matrix(embed[train_ind,])))
nb_anger <- classifier_nb
# save(nb_anger, "./nb_anger.Rdata")

#fear
classifier_nb <- naiveBayes(data1_prepared$hf_fear[train_ind] ~ ., data = as.data.frame(as.matrix(embed[train_ind,])))
nb_fear <- classifier_nb
# save(nb_fear, "./nb_fear.Rdata")

#disgust
classifier_nb <- naiveBayes(data1_prepared$hf_disgust[train_ind] ~ ., data = as.data.frame(as.matrix(embed[train_ind,])))
nb_disgust <- classifier_nb
# save(nb_disgust, "./nb_disgust.Rdata")

#sadness
classifier_nb <- naiveBayes(data1_prepared$hf_sadness[train_ind] ~ ., data = as.data.frame(as.matrix(embed[train_ind,])))
nb_sadness <- classifier_nb
# save(nb_sadness, "./nb_sadness.Rdata")

#joy
classifier_nb <- naiveBayes(data1_prepared$hf_joy[train_ind] ~ ., data = as.data.frame(as.matrix(embed[train_ind,])))
nb_joy <- classifier_nb
# save(nb_joy, "./nb_joy.Rdata")

#enthusiasm
classifier_nb <- naiveBayes(data1_prepared$hf_enthusiasm[train_ind] ~ ., data = as.data.frame(as.matrix(embed[train_ind,])))
nb_enthusiasm <- classifier_nb
# save(nb_enthusiasm, "./nb_enthusiasm.Rdata")

#pride
classifier_nb <- naiveBayes(data1_prepared$hf_pride[train_ind] ~ ., data = as.data.frame(as.matrix(embed[train_ind,])))
nb_pride <- classifier_nb
# save(nb_pride, "./nb_pride.Rdata")

#hope
classifier_nb <- naiveBayes(data1_prepared$hf_hope[train_ind] ~ ., data = as.data.frame(as.matrix(embed[train_ind,])))
nb_hope <- classifier_nb
# save(nb_hope, "./nb_hope.Rdata")



#### Random Forest Model #################################################
##### Create Word Embeddings #############################


# First, we build a corpus of our training and test data
cgcorpus <- corpus(data1_prepared$Text)
cgdfm <- dfm(cgcorpus, remove=stopwords("german"), verbose=TRUE, tolower = TRUE)

# Stemming
cgdfm <- dfm_wordstem(cgdfm, language = "german")

#First, we will convert the word embeddings to a data frame, and then we will 
#match the features from each document with their corresponding embeddings.

# extracting word embeddings for words in corpus (local word embeddings)
w2v <- readr::read_delim("./vec_ed_preprocessed.txt", 
                         skip=1, delim=" ", quote="",
                         col_names=c("word", paste0("V", 1:100)))

# Stemming
w2v$word <- text_tokens(w2v$word, stemmer = "de")

# Matching
w2v <- w2v[w2v$word %in% featnames(cgdfm),]


# creating new feature matrix for embeddings
embed <- matrix(NA, nrow=ndoc(cgdfm), ncol=100)
for (i in 1:ndoc(cgdfm)){
  if (i %% 100 == 0) message(i, '/', ndoc(cgdfm))
  # extract word counts
  vec <- as.numeric(cgdfm[i,])
  # keep words with counts of 1 or more
  doc_words <- featnames(cgdfm)[vec>0]
  # extract embeddings for those words
  embed_vec <- w2v[w2v$word %in% doc_words, 2:101]
  # aggregate from word- to document-level embeddings by taking AVG
  embed[i,] <- colMeans(embed_vec, na.rm=TRUE)
  # if no words in embeddings, simply set to 0
  if (nrow(embed_vec)==0) embed[i,] <- 0
}


##### Train Model #######################################################################
# Now, after we created embeddings per sentence, we can use these to train the random Forest model

#anger
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_anger[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_anger[-train_ind])
rfc <- randomForest(x = as.data.frame(as.matrix(x_train)), 
                    y = as.factor(data1_prepared$hf_anger[train_ind]), nTree = 100)

rfanger <- rfc
# save(rfanger, "./rfanger.Rdata")

#fear
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_fear[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_fear[-train_ind])
rfc <- randomForest(x = as.data.frame(as.matrix(x_train)), 
                    y = as.factor(data1_prepared$hf_fear[train_ind]), nTree = 100)
rffear <- rfc
# save(rffear, "./rffear.Rdata")

#disgust
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_disgust[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_disgust[-train_ind])
rfc <- randomForest(x = as.data.frame(as.matrix(x_train)), 
                    y = as.factor(data1_prepared$hf_disgust[train_ind]), nTree = 100)
rfdisgust <- rfc
# save(rfdisgust, "./rfdisgust.Rdata")

#sadness
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_sadness[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_sadness[-train_ind])
rfc <- randomForest(x = as.data.frame(as.matrix(x_train)), 
                    y = as.factor(data1_prepared$hf_sadness[train_ind]), nTree = 100)
rfsadness <- rfc
# save(rfsadness, "./rfsadness.Rdata")

#joy
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_joy[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_joy[-train_ind])
rfc <- randomForest(x = as.data.frame(as.matrix(x_train)), 
                    y = as.factor(data1_prepared$hf_joy[train_ind]), nTree = 100)
rfjoy <- rfc
# save(rfjoy, "./rfjoy.Rdata")

#enthusiasm
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_enthusiasm[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_enthusiasm[-train_ind])
rfc <- randomForest(x = as.data.frame(as.matrix(x_train)), 
                    y = as.factor(data1_prepared$hf_enthusiasm[train_ind]), nTree = 100)
rfenthusiasm <- rfc
# save(rfenthusiasm, "./rfenthusiasm.Rdata")

#pride
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_pride[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_pride[-train_ind])
rfc <- randomForest(x = as.data.frame(as.matrix(x_train)), 
                    y = as.factor(data1_prepared$hf_pride[train_ind]), nTree = 100)
rfpride <- rfc
# save(rfpride, "./rfpride.Rdata")

#hope
x_train <- embed[train_ind,]
y_train <- to_categorical(data1_prepared$hf_hope[train_ind])
x_test <- embed[-train_ind,]
y_test <- to_categorical(data1_prepared$hf_hope[-train_ind])
rfc <- randomForest(x = as.data.frame(as.matrix(x_train)), 
                    y = as.factor(data1_prepared$hf_hope[train_ind]), nTree = 100)
rfhope <- rfc
# save(rfhope, "./rfhope.Rdata")

#################################################################################
### END OF SCRIPT ####
#################################################################################

